Prognosis and Dissection of Immunosuppressive Microenvironment in Breast Cancer Based on Fatty Acid Metabolism-Related Signature

A novel fatty acid metabolism-related signature identifies MUC4 as a novel therapy target for esophageal squamous cell carcinoma

Fatty acid metabolism has been identified as an emerging hallmark of cancer, which was closely associated with cancer prognosis. Whether fatty acid metabolism-related genes (FMGs) signature play a more crucial role in biological behavior of esophageal squamous cell carcinoma (ESCC) prognosis remains unknown. Thus, we aimed to identify a reliable FMGs signature for assisting treatment decisions and prognosis evaluation of ESCC. In the present study, we conducted consensus clustering analysis on 259 publicly available ESCC samples. The clinical information was downloaded from The Cancer Genome Atlas (TCGA, 80 ESCC samples) and Gene Expression Omnibus (GEO) database (GSE53625, 179 ESCC samples). A consensus clustering arithmetic was used to determine the FMGs molecular subtypes, and survival outcomes and immune features were evaluated among the different subtypes. Kaplan-Meier analysis and the receiver operating characteristic (ROC) was applied to evaluate the reliability of the risk model in training cohort, validation cohort and all cohorts. A nomogram to predict patients' 1-year, 3-year and 5-year survival rate was also studied. Finally, CCK-8 assay, wound healing assay, and transwell assay were implemented to evaluate the inherent mechanisms of FMGs for tumorigenesis in ESCC. Two subtypes were identified by consensus clustering, of which cluster 2 is preferentially associated with poor prognosis, lower immune cell infiltration. A fatty acid (FA) metabolism-related risk model containing eight genes (FZD10, TACSTD2, MUC4, PDLIM1, PRSS12, BAALC, DNAJA2 and ALOX12B) was established. High-risk group patients displayed worse survival, higher stromal, immune and ESTIMATE scores than in the low-risk group. Moreover, a nomogram revealed good predictive ability of clinical outcomes in ESCC patients. The results of qRT-PCR analysis revealed that the MUC4 and BAALC had high expression level, and FZD10, PDLIM1, TACSTD2, ALOX12B had low expression level in ESCC cells. In vitro, silencing MUC4 remarkably inhibited ESCC cell proliferation, invasion and migration. Our study fills the gap of FMGs signature in predicting the prognosis of ESCC patients. These findings revealed that cluster subtypes and risk model of FMGs had effects on survival prediction, and were expected to be the potential promising targets for ESCC.

Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.

SLC27A2 is a potential immune biomarker for hematological tumors and significantly regulates the cell cycle progression of diffuse large B-cell lymphoma

BACKGROUND

Research on the fatty acid metabolism related gene SLC27A2 is currently mainly focused on solid tumors, and its mechanism of action in hematological tumors has not been reported.

METHOD

This study aims to explore the pathological and immune mechanisms of the fatty acid metabolism related gene SLC27A2 in hematological tumors and verify its functional role in hematological tumors through cell experiments to improve treatment decisions and clinical outcomes of hematological tumors.

RESULT

This study identified the fatty acid metabolism related gene SLC27A2 as a common differentially expressed gene between DLBCL and AML. Immune microenvironment analysis showed that SLC27A2 was significantly positively correlated with T cell CD4 + , T cell CD8 + , endothelial cells, macrophages, and NK cells in DLBCL. In AML, there is a significant negative correlation between SLC27A2 and B cells, T cell CD8 + , and macrophages. SLC27A2 participates in the immune process of hematological tumors through T cell CD8 + and macrophages. The GESA results indicate that high expression of SLC27A2 is mainly involved in the fatty acid pathway, immune pathway, and cell cycle pathway of DLBCL. The low expression of SLC27A2 is mainly involved in the immune pathway of AML. Therefore, SLC27A2 is mainly involved in the pathological mechanisms of hematological tumors through immune pathways, and cell experiments have also confirmed that SLC27A2 is involved in the regulation of DLBCL cells.

CONCLUSION

In summary, our research results comprehensively report for the first time the mechanism of action of SLC27A2 in the immune microenvironment of DLBCL and AML, and for the first time verify the cycle and apoptotic effects of the fatty acid related gene SLC27A2 in DLBCL cells through cell experiments. Research can help improve the treatment of AML and DLBCL patients.

Identifying SLC2A6 as the novel protective factor in breast cancer by TP53-related genes affecting M1 macrophage infiltration

The high heterogeneity of breast cancer (BC) caused by pathogenic gene mutations poses a challenge to immunotherapy, but the underlying mechanism remains unknown. The difference in the infiltration of M1 macrophages induced by TP53 mutations has a significant impact on BC immunotherapy. The aim of this study was to develop a TP53-related M1 macrophage infiltration molecular typing risk signature in BC and evaluate the biological functions of the key gene to find new immunotherapy biomarkers. Weighted correlation network analysis (WGCNA) and negative matrix factorization (NMF) were used for distinguishing BC subtypes. The signature and the nomogram were both constructed and evaluated. Biological functions of the novel signature gene SLC2A6 were confirmed through in vitro and in vivo experiments. RNA-Sequencing and protein profiling were used for detecting the possible mechanism of SLC2A6. The results suggested that four BC subtypes were distinguished by TP53-related genes that affect M1 macrophage infiltration. The signature constructed by molecular typing characteristics could evaluate BC's clinical features and tumor microenvironment. The nomogram could accurately predict the prognosis. The signature gene SLC2A6 was found to have an abnormally low expression in tumor tissues. Overexpression of SLC2A6 could inhibit proliferation, promote mitochondrial damage, and result in apoptosis of tumor cells. The HSP70 family member protein HSPA6 could bind with SLC2A6 and increase with the increased expression of SLC2A6. In summary, the risk signature provides a reference for BC risk assessment, and the signature gene SLC2A6 could act as a tumor suppressor in BC.

Characterization and validation of fatty acid metabolism-related genes predicting prognosis, immune infiltration, and drug sensitivity in endometrial cancer

Endometrial cancer is considered to be the second most common tumor of the female reproductive system, and patients diagnosed with advanced endometrial cancer have a poor prognosis. The influence of fatty acid metabolism in the prognosis of patients with endometrial cancer remains unclear. We constructed a prognostic risk model using transcriptome sequencing data of endometrial cancer and clinical information of patients from The Cancer Genome Atlas (TCGA) database via least absolute shrinkage and selection operator regression analysis. The tumor immune microenvironment was analyzed using the CIBERSORT algorithm, followed by functional analysis and immunotherapy efficacy prediction by gene set variation analysis. The role of model genes in regulating endometrial cancer in vitro was verified by CCK-8, colony formation, wound healing, and transabdominal invasion assays, and verified in vivo by subcutaneous tumor transplantation in nude mice. A prognostic model containing 14 genes was constructed and validated in 3 cohorts and clinical samples. The results showed differences in the infiltration of immune cells between the high-risk and low-risk groups, and that the high-risk group may respond better to immunotherapy. Experiments in vitro confirmed that knockdown of epoxide hydrolase 2 (EPHX2) and acyl-CoA oxidase like (ACOXL) had an inhibitory effect on EC cells, as did overexpression of hematopoietic prostaglandin D synthase (HPGDS). The same results were obtained in experiments in vivo. Prognostic models related to fatty acid metabolism can be used for the risk assessment of endometrial cancer patients. Experiments in vitro and in vivo confirmed that the key genes HPGDS, EPHX2, and ACOXL in the prognostic model may affect the development of endometrial cancer.

Investigation of fatty acid metabolism in chronic lymphocytic leukemia to guide clinical outcome and therapy

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the West. With CLL's heterogeneity, some people still develop disease refractory and relapse despite advances in treatment. Thus, early diagnosis and treatment of high-risk CLL patients is critical. Fatty acid (FA) metabolism contributes to tumorigenesis, progression, and therapy resistance through enhanced lipid synthesis, storage, and catabolism. In this study, we aimed to construct a prognostic model to improve the risk stratification of CLL and reveal the link between FA metabolism and CLL. The differentially expressed FA metabolism-related genes (FMGs) in CLL were filtered through univariate Cox regression analysis based on public databases. Functional enrichment was examined using prognostic FA metabolism-related gene enrichment analysis. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) estimated immune infiltration score and immune-related pathways. Pearson's correlation analysis investigated FA metabolism-related genes and drug sensitivity. A novel prognostic model was built using least absolute shrinkage and selection operator (LASSO) Cox algorithms. This validation cohort included 36 CLL patients from our center. We obtained CLL RNA microarray profiles from public databases and identified 15 prognostic-related FMGs. CLL patients were divided into two molecular clusters based on the expression of FMGs. The Kaplan-Meier analysis revealed a significant difference in TFS (P < 0.001) and OS (P < 0.001) between the two clusters. KEGG functional analysis showed that several pathways were enriched, including the chemokine and immune-related signaling pathways. In the training and validation cohorts, patients with higher FA metabolism-related prognostic index (FAPI) levels had worse outcomes. Finally, a novel nomogram prognostic model including CLL international prognostic index (CLL-IPI) was constructed, exhibiting reliable effectiveness and accuracy. In conclusion, we established a reliable predictive signature based on FA metabolism-related genes and constructed a novel nomogram prognostic model, supporting the potential preclinical implications of FA metabolism in CLL research.

Progressions of the correlation between lipid metabolism and immune infiltration characteristics in gastric cancer and identification of BCHE as a potential biomarker

Background

Globally, gastric cancer (GC) is a category of prevalent malignant tumors. Its high occurrence and fatality rates represent a severe threat to public health. According to recent research, lipid metabolism (LM) reprogramming impacts immune cells' ordinary function and is critical for the onset and development of cancer. Consequently, the article conducted a sophisticated bioinformatics analysis to explore the potential connection between LM and GC.

Methods

We first undertook a differential analysis of the TCGA queue to recognize lipid metabolism-related genes (LRGs) that are differentially expressed. Subsequently, we utilized the LASSO and Cox regression analyses to create a predictive signature and validated it with the GSE15459 cohort. Furthermore, we examined somatic mutations, immune checkpoints, tumor immune dysfunction and exclusion (TIDE), and drug sensitivity analyses to forecast the signature's immunotherapy responses.

Results

Kaplan-Meier (K-M) curves exhibited considerably longer OS and PFS (p<0.001) of the low-risk (LR) group. PCA analysis and ROC curves evaluated the model's predictive efficacy. Additionally, GSEA analysis demonstrated that a multitude of carcinogenic and matrix-related pathways were much in the high-risk (HR) group. We then developed a nomogram to enhance its clinical practicality, and we quantitatively analyzed tumor-infiltrating immune cells (TIICs) using the CIBERSORT and ssGSEA algorithms. The low-risk group has a lower likelihood of immune escape and more effective in chemotherapy and immunotherapy. Eventually, we selected BCHE as a potential biomarker for further research and validated its expression. Next, we conducted a series of cell experiments (including CCK-8 assay, Colony formation assay, wound healing assay and Transwell assays) to prove the impact of BCHE on gastric cancer biological behavior.

Discussion

Our research illustrated the possible consequences of lipid metabolism in GC, and we identified BCHE as a potential therapeutic target for GC. The LRG-based signature could independently forecast the outcome of GC patients and guide personalized therapy.

Establish a novel tumor budding-related signature to predict prognosis and guide clinical therapy in colorectal cancer

Tumor budding is a long-established independent adverse prognostic marker for colorectal cancer (CRC), yet assessment of tumor budding was not reproducible. Therefore, development of precise diagnostic approaches to tumor budding is in demand. In this study, we first performed bioinformatic analysis in our single-center CRC patients' cohort (n = 84) and identified tumor budding-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify hub genes and construct a prognostic signature. Nomogram model was used to identified hub genes score for tumor budding, and the receiver operating characteristic (ROC) curve and calibration plot indicated high accuracy and stability of hub gene score for predicted the prognosis of CRC. The association between budding-associated hub genes and score and prognosis of CRC were further verified in TCGA CRC cohort (n = 342). Then gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to explore the signaling pathways related to the tumor budding and validated by immunohistochemistry (IHC) of our clinical samples. Subsequently, immune infiltration analysis demonstrated that there was a high correlation between hub genes score and M2-like macrophages infiltrated in tumor tissue. In addition, somatic mutation and chemotherapeutic response prediction were analyzed based on the risk signature. In summary, we established a tumor budding diagnostic molecular model, which can improve tumor budding assessment and provides a promising novel molecular marker for immunotherapy and prognosis of CRC.

Dissection of FOXO1-Induced LYPLAL1-DT Impeding Triple-Negative Breast Cancer Progression via Mediating hnRNPK/β-Catenin Complex

Triple-negative breast cancer (TNBC) is considered as the most hazardous subtype of breast cancer owing to its accelerated progression, enormous metastatic potential, and refractoriness to standard treatments. Long noncoding RNAs (lncRNAs) are extremely intricate in tumorigenesis and cancerous metastasis. Nonetheless, their roles in the initiation and augmentation of TNBC remain elusive. Here, in silico analysis and validation experiments were utilized to analyze the expression pattern of clinically effective lncRNAs in TNBC, among which a protective lncRNA LYPLAL1-DT was essentially curbed in TNBC samples and indicated a favorable prognosis. Gain- and loss-of-function assays elucidated that LYPLAL1-DT considerably attenuated the proliferative and metastatic properties along with epithelial-mesenchymal transition of TNBC cells. Moreover, forkhead box O1 (FOXO1) was validated to modulate the transcription of LYPLAL1-DT. Mechanistically, LYPLAL1-DT impinged on the malignancy of TNBC mainly by restraining the aberrant reactivation of the Wnt/β-catenin signaling pathway, explicitly destabilizing and diminishing β-catenin protein by interacting with heterogeneous nuclear ribonucleoprotein K (hnRNPK) and constricting the formation of the hnRNPK/β-catenin complex. Conclusively, our present research revealed the anti-oncogenic effects of LYPLAL1-DT in TNBC, unraveling the molecular mechanisms of the FOXO1/LYPLAL1-DT/hnRNPK/β-catenin signaling axis, which shed innovative light on the potential curative medicine of TNBC.

HER2 low expression breast cancer subtyping and their correlation with prognosis and immune landscape based on the histone modification related genes

Human epidermal growth factor receptor 2 (HER2) plays an important role in diagnosis and treatment of breast cancer (BRCA). The histone modification has been found to be related to the progression of cancer. This study aimed to probe the low HER2 expression BRCA heterogeneity by histone modification genes. The BRCA data and cell lines were collected from The Cancer Genome Atlas database. Weighted gene co-expression network analysis and non-negative matrix factorization clustering were jointly applied to obtain BRCA clusters. The expression of hub histone modification gene was detected using western blot assay. The gene ontology term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to reveal functional information. The overall survival analysis was performed using survival and survminer packages, and the immune landscape was mainly analyzed using CIBERSORT software. Totally 43 histone modification genes correlated with survival of BRCA patients with HER2 low expression were screened. Based on these 43 histone modification genes, the BRCA samples were classified into cluster1, cluster2 and cluster3. Histone modification gene NFKBIZ exhibited high expression, while RAD51 demonstrated low expression in low HER2 expression BRCA cell. Cluster1 exhibited the best prognosis, while cluster3 had the worse outcomes. Tumor mutational burden (TMB) was remarkably increased in cluster3 group compared to cluster1 and cluster2. Moreover, the relative proportion of 16 immune cell infiltration and 8 immune checkpoint expression were remarkably differential among cluster1, cluster2 and cluster3, and the drug sensitivity exhibited difference among cluster1, cluster2 and cluster3 in BRCA patients with low HER2 expression. This study identified three HER2 low expression BRCA clusters with different characteristics based on histone modification genes. The TMB, immune cell infiltration, immune checkpoints and drug sensitivity were different among the three clusters.

AURKAIP1 actuates tumor progression through stabilizing DDX5 in triple negative breast cancer

Aurora-A kinase interacting protein 1 (AURKAIP1) has been proved to take an intermediary role in cancer by functioning as a negative regulator of Aurora-A kinase. However, it remains unclear whether and how AURKAIP1 itself would directly engage in regulating malignancies. The expression levels of AURKAIP1 were detected in triple negative breast cancer (TNBC) by immunohistochemistry and western blots. The CCK8, colony formation assays and nude mouse model were conducted to determine cell proliferation whereas transwell and wound healing assays were performed to observe cell migration. The interaction of AURKAIP1 and DEAD-box helicase 5 (DDX5) were verified through co-immunoprecipitation and successively western blots. From the results, we found that AURKAIP1 was explicitly upregulated in TNBC, which was positively associated with tumor size, lymph node metastases, pathological stage and unfavorable prognosis. AURKAIP1 silencing markedly inhibited TNBC cell proliferation and migration in vitro and in vivo. AURKAIP1 directly interacted with and stabilized DDX5 protein by preventing ubiquitination and degradation, and DDX5 overexpression successfully reversed proliferation inhibition induced by knockdown of AURKAIP1. Consequently, AURKAIP1 silencing suppressed the activity of Wnt/β-catenin signaling in a DDX5-dependent manner. Our study may primarily disclose the molecular mechanism by which AURKAIP1/DDX5/β-catenin axis modulated TNBC progression, indicating that AURKAIP1 might serve as a therapeutic target as well as a TNBC-specific biomarker for prognosis.

Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes

Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein-protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.

Molecular interaction of metastasis suppressor genes and tumor microenvironment in breast cancer

Breast cancer (BC) is a leading cause of cancer-related deaths in women worldwide where the process of metastasis is a major contributor to the mortality associated with this disease. Metastasis suppressor genes are a group of genes that play a crucial role in preventing or inhibiting the spread of cancer cells. They suppress the metastasis process by inhibiting colonization and by inducing dormancy. These genes function by regulating various cellular processes in the tumor microenvironment (TME), such as cell adhesion, invasion, migration, and angiogenesis. Dysregulation of metastasis suppressor genes can lead to the acquisition of an invasive and metastatic phenotype and lead to poor prognostic outcomes. The components of the TME generally play a necessary in the metastasis progression of tumor cells. This review has identified and elaborated on the role of a few metastatic suppressors associated with the TME that have been shown to inhibit metastasis in BC by different mechanisms, such as blocking certain cell signaling molecules involved in cancer cell migration, invasion, enhancing immune surveillance of cancer cells, and promoting the formation of a protective extracellular matrix (ECM). Understanding the interaction of metastatic suppressor genes and the components of TME has important implications for the development of novel therapeutic strategies to target the metastatic cascade. Targeting these genes or their downstream signaling pathways offers a promising approach to inhibiting the spread of cancer cells and improves patient outcomes.

Identifying prognostic markers in spatially heterogeneous breast cancer microenvironment

To gain deeper insights into the microenvironment of breast cancer, we utilized GeoMx Digital Spatial Profiling (DSP) technology to analyze transcripts from 107 regions of interest in 65 untreated breast cancer tissue samples. Our study revealed spatial heterogeneity in the expression of marker genes in tumor cell enriched, immune cell enriched, and normal epithelial areas. We evaluated a total of 55 prognostic markers in tumor cell enriched regions and 15 in immune cell enriched regions, identifying that tumor cell enriched regions had higher levels of follicular helper T cells, resting dendritic cells, and plasma cells than immune cell enriched regions, while the levels of resting CD4 memory in T cells and regulatory (Treg) T cells were lower. Additionally, we analyzed the heterogeneity of HLA gene families, immunological checkpoints, and metabolic genes in these areas. Through univariate Cox analysis, we identified 5 prognosis-related metabolic genes. Furthermore, we conducted immunostaining experiments, including EMILIN2, SURF4, and LYPLA1, to verify our findings. Our investigation into the spatial heterogeneity of the breast cancer tumor environment has led to the discovery of specific diagnostic and prognostic markers in breast cancer.

Integrating Single-Cell RNA-Seq and Bulk RNA-Seq Data to Explore the Key Role of Fatty Acid Metabolism in Breast Cancer

Cancer immune escape is associated with the metabolic reprogramming of the various infiltrating cells in the tumor microenvironment (TME), and combining metabolic targets with immunotherapy shows great promise for improving clinical outcomes. Among all metabolic processes, lipid metabolism, especially fatty acid metabolism (FAM), plays a major role in cancer cell survival, migration, and proliferation. However, the mechanisms and functions of FAM in the tumor immune microenvironment remain poorly understood. We screened 309 fatty acid metabolism-related genes (FMGs) for differential expression, identifying 121 differentially expressed genes. Univariate Cox regression models in The Cancer Genome Atlas (TCGA) database were then utilized to identify the 15 FMGs associated with overall survival. We systematically evaluated the correlation between FMGs' modification patterns and the TME, prognosis, and immunotherapy. The FMGsScore was constructed to quantify the FMG modification patterns using principal component analysis. Three clusters based on FMGs were demonstrated in breast cancer, with three patterns of distinct immune cell infiltration and biological behavior. An FMGsScore signature was constructed to reveal that patients with a low FMGsScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, better survival advantage, and were more sensitive to immunotherapy than those with a high FMGsScore. Finally, the expression and function of the signature key gene NDUFAB1 were examined by in vitro experiments. This study significantly demonstrates the substantial impact of FMGs on the immune microenvironment of breast cancer, and that FMGsScores can be used to guide the prediction of immunotherapy efficacy in breast cancer patients. In vitro experiments, knockdown of the NDUFAB1 gene resulted in reduced proliferation and migration of MCF-7 and MDA-MB-231 cell lines.

Prognosis analysis and validation of lipid metabolism-associated lncRNAs and tumor immune microenvironment in bladder cancer

BACKGROUND

Numerous types of research revealed that long noncoding RNAs (lncRNAs) played a significant role in immune response and the tumor microenvironment of bladder cancer (BLCA). Dysregulated lipid metabolism is considered to be one of the major risk factors for BLCA, the study aimed to detect the lipid metabolism-related lncRNAs (LMRLs) along with their potential prognostic values and immune correlations in BLCA.

METHODS

We collected lipid metabolism-related genes, expression profiles, and clinical information on BLCA from the Molecular Signature Database (MSigDB) and the TCGA database, respectively. Differentially expressed lipid metabolism genes (DE-LMRGs) and differentially expressed long non-coding RNAs (DE-lncRNAs) were selected using the limma package. Spearman correlation analysis was employed to explore the correlations between DE-lncRNAs and DE-LMRGs and to further develop protein-protein interaction (PPI) networks and perform mutational analysis. The least absolute shrinkage and selection operator (LASSO) and univariate Cox analysis were then employed to construct a prognostic risk model. The performance of the model was evaluated using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and consistency indices. In addition, we downloaded the GSE31684 dataset for external validation of the prognostic signature. Moreover, we explored the association of the risk model with immune cell infiltration and chemotherapy response analysis to reveal the tumor immune microenvironment of BLCA. Finally, RT-qPCR was utilized to validate the expression of prognostic genes.

RESULTS

A total of 48 DE-LncRNAs and 33 DE-LMRGs were found to be robustly correlated, and were used to construct a lncRNA-mRNA co-expression network, in which ACACB, ACOX2, and BCHE showed high mutation rates. Then, a risk model based on three LMRLs (RP11-465B22.8, MIR100HG, and LINC00865) was constructed. The risk model effectively distinguished between the clinical outcomes of BLCA patients, with high-risk scores indicating a worse prognosis and with substantial prognostic prediction accuracy. The model's results were consistent in the GSE31684 dataset. In addition, a nomogram was constructed based on the risk score, age, pathological T-stage, and pathological N-stage, which showed robust predictive power. Immune landscape analysis indicated that the risk model was significantly associated with T-cell CD4 memory activation, M1 macrophage, M2 macrophage, dendritic cell activation, and T-cell regulatory. We predicted that 49 drugs would perform satisfactorily in the high-risk group. Additionally, we found five m6A regulators associated with the high- and low-risk groups, suggesting that upstream regulation of LncRNA could be a novel target for BLCA treatment. Finally, RT-qPCR showed that RP11-465B22.8 was highly expressed in BLCA, while MIR100HG and LINC00865 were downregulated in BLCA.

CONCLUSION

Our findings suggest that the three LMRLs may serve as potential prognostic and immunotherapeutic biomarkers in BLCA. In addition, our study provides new ideas for understanding the pathogenic mechanisms and developing therapeutic strategies for BLCA patients.

GLS as a diagnostic biomarker in breast cancer: in-silico, in-situ, and in-vitro insights

Background

Recently, a novel programmed cell death mechanism, Cuproptosis, has been discovered and found to play an important role in the development and progression of diverse tumors. In the present study, we comprehensively investigated the core gene of this mechanism, GLS, in breast cancer.

Materials and methods

Bulk RNA sequencing data were curated from the TCGA repository to investigate the aberrant expression of GLS over diverse cancer types. Then, we examined its efficacy as a diagnostic biomarker in breast cancer by Area Under Curve (AUC) of the Receiver Operative Characteristic (ROC) curve. Furthermore, by applying siRNA technique, we knocked down the GLS expression level in cancerous cell lines, measuring the corresponding effects on cell proliferation and metastasis. Afterward, we explored the potential implications of GLS expression in the tumor immune microenvironment quantitatively by using several R packages and algorithms, including ESTIMATE, CIBERSORT, etc.

Results

Pan-cancer analysis suggested that GLS was aberrantly over-expressed in many cancer types, with breast cancer being typical. More in-depth analyses revealed the expression of GLS exerted a high ROC-AUC value in breast cancer diagnosis. Through the knock-down of GLS expression, it was found that GLS expression was strongly relevant to the growth and metastasis of tumor. Furthermore, it was also found to be correlated with the immune tumor microenvironment.

Conclusion

We highlighted that GLS expression might be applicable as a diagnostic biomarker in breast cancer and possess significant implications in the growth and metastasis of tumor and the immune tumor microenvironment, sharing new insights into ontological and personalized medicine.

Identification of disulfidptosis related subtypes, characterization of tumor microenvironment infiltration, and development of DRG prognostic prediction model in RCC, in which MSH3 is a key gene during disulfidptosis

Disulfidptosis is a newly discovered mode of cell death induced by disulfide stress. However, the prognostic value of disulfidptosis-related genes (DRGs) in renal cell carcinoma (RCC) remains to be further elucidated. In this study, consistent cluster analysis was used to classify 571 RCC samples into three DRG-related subtypes based on changes in DRGs expression. Through univariate regression analysis and LASSO-Cox regression analysis of differentially expressed genes (DEGs) among three subtypes, we constructed and validated a DRG risk score to predict the prognosis of patients with RCC, while also identifying three gene subtypes. Analysis of DRG risk score, clinical characteristics, tumor microenvironment (TME), somatic cell mutations, and immunotherapy sensitivity revealed significant correlations between them. A series of studies have shown that MSH3 can be a potential biomarker of RCC, and its low expression is associated with poor prognosis in patients with RCC. Last but not least, overexpression of MSH3 promotes cell death in two RCC cell lines under glucose starvation conditions, indicating that MSH3 is a key gene in the process of cell disulfidptosis. In summary, we identify potential mechanism of RCC progression through DRGs -related tumor microenvironment remodeling. In addition, this study has successfully established a new disulfidptosis-related genes prediction model and discovered a key gene MSH3. They may be new prognostic biomarkers for RCC patients, provide new insights for the treatment of RCC patients, and may inspire new methods for the diagnosis and treatment of RCC patients.

PEG-functionalized black phosphorus quantum dots as stable and biocompatible electrochemiluminescence luminophores for sensitive detection of tumor biomarker

Despite black phosphorous (BP) QDs possess the merits of size-tunable band-gap, high electron mobility, and intrinsic defects, the spontaneous agglomeration and rapid oxidation of BP QDs in aqueous solution caused low electrochemiluminescence (ECL) efficiency and unstable ECL signal, which confined its further application of biological analysis. Herein, polyethylene glycol-functionalized BP QDs (PEG@BP QDs) were prepared showing an efficient and stable ECL response, which is attributed to the fact that PEG as protectant not only effectively prevented the spontaneous agglomeration, but also restrained the rapid oxidation of BP QDs in aqueous solution. As proof-of-concept, PEG@BP QDs were used as an efficient ECL emitter to combine with palindrome amplification-induced DNA walker to construct a sensitive ECL aptasensing platform for detecting cancer marker mucin 1 (MUC1). Interestingly, with the aid of positively charged thiolated PEG, the reaction rate of DNA walker on the electrode interface was clearly increased for the recovery of the ECL signal. The ECL aptasensor provides sensitive determination with the detection limit of 16.5 fg/mL. The proposed strategy paves a path for the development of efficient and stable ECL nanomaterials to construct biosensors for biosensing and clinical diagnosis.

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

OBJECTIVES

Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m¹A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m¹A RNA methylation-related genes in BRCA is indistinct.

METHODS

The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m¹A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m¹A-related genes were selected through overlapping m¹A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m¹A score subgroups. The m¹A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).

RESULTS

Eighty-five differentially expressed m¹A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.

CONCLUSIONS

An m¹A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

A novel circadian cycle-related gene signature for prognosis prediction of patients with breast cancer

The extensive and intricate relationships between circadian rhythm and cancer have been reported in numerous studies. However, in breast cancer (BC), the potential role of circadian clock-related genes (CCRGs) in prognosis prediction has not been fully clarified. The transcriptome data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. A CCRGs-based risk signature was established by differential expression analysis, univariate, Lasso and multivariate Cox regression analyses. we conducted a gene set enrichment analysis (GSEA) between groups. A nomogram integrating independent clinical factors and risk score was generated and evaluated by calibration curves and decision curve analysis (DCA). Differentially expression analysis revealed 80 differentially expressed CCRGs, and 27 of them were significantly associated with the overall survival (OS) of BC. BC can be classified into 4 molecular subtypes with significant differences in prognosis based on the 27 CCRGs. Three prognostic CCRGs, including desmocollin 1 (DSC1), LEF1, and protocadherin 9 (PCDH9), were identified to be independent risk factors of BC prognosis and were used to construct a risk score model. BC patients were divided into high- and low-risk groups, and there were significant differences in prognosis between the 2 groups both in the training and validation cohorts. It was found that patients in different groups of race, status, or T stage had significant levels of risk score. Furthermore, patients of different risk levels exhibit varying degrees of sensitivity to vinorelbine, lapatinib, metformin, and vinblastine. GSEA showed that in the high-risk group, immune response-related activities were dramatically repressed whereas cilium-related processes were significantly stimulated. Cox regression analysis demonstrated that age, N stage, radiotherapy and the risk score were independent prognostic risk factors of BC, and a nomogram was established based on these variables. The nomogram exerted a favorable concordance index (0.798) as well as calibration performance, which strongly supports the clinical application of the nomogram. Our study indicated the disruption of the expression of CCRGs in BC and built a favorable prognostic risk model based on 3 independent prognostic CCRGs. These genes may be applied as candidate molecular targets for the diagnosis and therapy of BC.

Development and validation of robust metabolism-related gene signature in the prognostic prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism-related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism-related genes associated with prognosis, a risk score based on metabolism-related genes was formulated via the LASSO-Cox and LASSO-pcvl algorithms. According to the risk score, patients were stratified into low- and high-risk groups, and further analysis and validation were accordingly conducted. The results revealed that high-risk patients had a significantly worse 5-year overall survival (OS) than low-risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302-0.651; p < 0.001) This observation was confirmed in the external TCGA-LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299-0.681; p < 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism-based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.

Identification of cuproptosis hub genes contributing to the immune microenvironment in ulcerative colitis using bioinformatic analysis and experimental verification

Instruction

Ulcerative colitis (UC) can cause a variety of immune-mediated intestinal dysfunctions and is a significant model of inflammatory bowel disease (IBD). Colorectal cancer (CRC) mostly occurs in patients with ulcerative colitis. Cuproptosis is a type of procedural death that is associated with different types of diseases to various degrees.

Methods

We used a combination of bioinformatic prediction and experimental verification to study the correlation between copper poisoning and UC. We used the Gene Expression Omnibus database to obtain disease gene expression data and then identified relevant genes involved in various expression levels in normal and UC samples. The Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed to cluster the genes that are highly responsible and find the central interaction in gene crosstalk. Notably, DLD, DLAT, and PDHA1 were present in high-scoring PPI networks. In addition, hub gene expression information in UC tissues was integrated to estimate the relationship between UC copper poisoning and the immune environment.

Results

In our study, the expression of DLD, DLAT, and PDHA1 in UC tissues was lower than that in normal tissues. The key genes associated with cuproptosis have therapeutic effects on immune infiltration. We verified the expression of DLD, DLAT, and PDHA1 using real-time quantitative polymerase chain reaction in mouse models of UC induced by DSS.

Discussion

Notably, this study clearly indicates that bioinformatic analysis performed to verify the experimental methods provides evidence that cuproptosis is associated with UC. This finding suggests that immune cell infiltration in UC patients is associated with cuproptosis. The key genes associated with cuproptosis can be helpful for discovering the molecular mechanism of UC, thus facilitating the improvement of UC treatment and preventing the associated CRC.

Characterization of protein-based risk signature to predict prognosis and evaluate the tumor immune environment in breast cancer

BACKGROUND

Proteomics profiles have enabled a systematic insight into the prognosis of cancer. This study aimed to establish a valuable protein-based risk signature to assess the prognosis and immune status in patients with breast cancer (BC).

METHODS

Protein expression profile, RNA expression data, and clinical information were acquired from The Cancer Genome Atlas (TCGA). The whole cohort was randomly split into two cohorts, one for establishing the risk signature and the other for testing. Univariate Cox analysis and Least absolute shrinkage and selection operator (LASSO) Cox regression were utilized to construct the protein-based risk signature. All cohorts were divided into high- and low-risk groups, which were applied to investigate the clinical relevance, tumor microenvironment, and therapeutic response.

RESULTS

The prognostic proteomics signature was established based on prognostic proteins, thus categorizing patients into low-risk and high-risk groups with different prognoses. A predictive nomogram was also developed to predict 1, 3, and 5-year survival possibility for BC patients, and the calibration curves confirmed the predictive significance of this signature. Afterward, the low-risk group displayed higher immune activities, immune checkpoint expression, and immunotherapeutic response. Moreover, GSEA analysis indicated that immune-associated pathways were rich in the low-risk group. Additionally, this prognostic signature demonstrated potential predict significance for chemotherapeutic agents.

CONCLUSION

This study established an effective prognostic proteomics signature with reliable predictive performance for survival, immune activity, and drug sensitivity. It might provide a novel perspective into the protein function in BC, and guide the individual treatment strategies for BC patients.

Polysulfide Serves as a Hallmark of Desmoplastic Reaction to Differentially Diagnose Ductal Carcinoma In Situ and Invasive Breast Cancer by SERS Imaging

Pathological examination of formalin-fixed paraffin-embedded (FFPE) needle-biopsied samples by certified pathologists represents the gold standard for differential diagnosis between ductal carcinoma in situ (DCIS) and invasive breast cancers (IBC), while information of marker metabolites in the samples is lost in the samples. Infrared laser-scanning large-area surface-enhanced Raman spectroscopy (SERS) equipped with gold-nanoparticle-based SERS substrate enables us to visualize metabolites in fresh-frozen needle-biopsied samples with spatial matching between SERS and HE staining images with pathological annotations. DCIS (n = 14) and IBC (n = 32) samples generated many different SERS peaks in finger-print regions of SERS spectra among pathologically annotated lesions including cancer cell nests and the surrounding stroma. The results showed that SERS peaks in IBC stroma exhibit significantly increased polysulfide that coincides with decreased hypotaurine as compared with DCIS, suggesting that alterations of these redox metabolites account for fingerprints of desmoplastic reactions to distinguish IBC from DCIS. Furthermore, the application of supervised machine learning to the stroma-specific multiple SERS signals enables us to support automated differential diagnosis with high accuracy. The results suggest that SERS-derived biochemical fingerprints derived from redox metabolites account for a hallmark of desmoplastic reaction of IBC that is absent in DCIS, and thus, they serve as a useful method for precision diagnosis in breast cancer.

Prognostic significance and immune landscape of a fatty acid metabolism-related gene signature in colon adenocarcinoma

Background: Fatty acid metabolism (FAM), as a hallmark of caner, plays important roles in tumor initiation and carcinogenesis. However, the significance of fatty acid metabolism-related genes in colon adenocarcinoma (COAD) are largely unknown. Methods: RNA sequencing data and clinical information were downloaded from the Cancer Genome Atlas (TCGA) cohort. Univariate and multivariate Cox regression analyses were utilized to construct a fatty acid metabolism-related gene signature. Kaplan-Meier survival and receiver operating characteristic (ROC) analyses were used to verify the performance of this signature. GEO datasets were applied to validate the signature. Maftools package was utilized to analyze the mutation profiles of this signature. Correlation between the risk signature and stemness scores was compared by RNA stemness score (RNAss). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set variation analysis (GSVA) were performed to explore the potential functions and signaling pathways. Immune landscape of the signature was explored by analyzing different immune cells infiltration, immune functions and microsatellite instability. A nomogram was constructed by combining the risk signature and multiple clinical factors. Expression levels and prognostic values of the risk genes were revealed in the cancer genome atlas and GEO databases. Moreover, the expression the risk genes were measured in cell lines using real time quantitative PCR (qRT-PCR). Results: Eight fatty acid metabolism-related genes (CD36, ENO3, MORC2, PTGR1, SUCLG2, ELOVL3, ELOVL6 and CPT2) were used to construct a risk signature. This signature demonstrated better prognostic value than other clinicopathological parameters, with AUC value was 0.734 according to the cancer genome atlas database. There was negative correlation between the riskscore and RNA stemness score. The patients in the high-risk group demonstrated higher infiltration of M0 macrophages, and less infiltration of activated CD4 memory T cells and Eosinophils. There were more MSI patients in the high-risk group than those in the low-risk group (38% vs. 30%). The risk scores of patients in the MSI group were slightly higher than those in the microsatellite stability group. Gene ontology, kyoto encyclopedia of genes and genomes and gene set variation analysis enrichment analyses showed that several metabolism-related functions and signaling pathways were enriched. A nomogram showed good predictive capability of the signature. Moreover, qRT-PCR revealed upregulated expression of ENO3, MORC2, SUCLG2 and ELOVL6, and downregulated expression of CPT2 in all examined colon adenocarcinoma cell lines. Conclusion: This study provided novel insights into a fatty acid metabolism-related signature in the prognosis an immune landscape of colon adenocarcinoma patients.

The Pan-Cancer Multi-Omics Landscape of FOXO Family Relevant to Clinical Outcome and Drug Resistance

The forkhead box O (FOXO) transcription factors (TFs) family are frequently mutated, deleted, or amplified in various human cancers, making them attractive candidates for therapy. However, their roles in pan-cancer remain unclear. Here, we evaluated the expression, prognostic value, mutation, methylation, and clinical features of four FOXO family genes (FOXO1, FOXO3, FOXO4, and FOXO6) in 33 types of cancers based on the Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases. We used a single sample gene set enrichment analysis (ssGSEA) algorithm to establish a novel index called "FOXOs score". Moreover, we investigated the association between the FOXOs score and tumor microenvironment (TME), the responses to multiple treatments, along with drug resistance. We found that the FOXO family genes participated in tumor progression and were related to the prognosis in various types of cancer. We calculated the FOXOs score and found that it was significantly correlated with multiple malignant pathways in pan-cancer, including Wnt/beta-catenin signaling, TGF-beta signaling, and hedgehog signaling. In addition, the FOXOs score was also associated with multiple immune-related characteristics. Furthermore, the FOXOs score was sensitive for predicting the efficacy of diverse treatments in multiple cancers, especially immunotherapy. In conclusion, FOXO family genes were vital in pan-cancer and were strongly correlated with the TME. A high FOXOs score indicated an excellent immune-activated TME and sensitivity to multiple treatments. Hence, the FOXOs score might potentially be used as a biomarker in patients with a tumor.

Prognosis stratification in breast cancer and characterization of immunosuppressive microenvironment through a pyrimidine metabolism-related signature

Pyrimidine metabolism is a hallmark of cancer and will soon become an essential part of cancer therapy. In the tumor microenvironment, cells reprogram pyrimidine metabolism intrinsically and extracellularly, thereby promoting tumorigenesis. Metabolites in pyrimidine metabolism have a significant impact on promoting cancer advancement and modulating immune system responses. In preclinical studies and practical clinical applications, critical targets in pyrimidine metabolism are acted upon by drugs to exert promising therapeutic effects on tumors. However, the pyrimidine metabolism in breast cancer (BC) is still largely underexplored. In this study, 163 credible pyrimidine metabolism-related genes (PMGs) were retrieved, and their somatic mutations and expression levels were determined. In addition, by using The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases, 12 PMGs related to the overall survival (OS) were determined using the univariate Cox regression analysis. Subsequently, by performing the LASSO Cox hazards regression analysis in the 12 PMGs in TCGA-BRCA dataset, we developed a prognosis nomogram using eight OS-related PMGs and then verified the same in the METABRIC, GSE96058, GSE20685, GSE42568 and GSE86166 data. Moreover, we validated relationships between the pyrimidine metabolism index (PMI) and the survival probability of patients, essential clinical parameters, including the TNM stage and the PAM50 subtypes. Next, we verified the predictive capability of the optimum model, including the signature, the PAM50 subtype, and age, using ROC analysis and calibration curve, and compared it with other single clinical factors for the predictive power of benefit using decision curve analysis. Finally, we investigated the potential effects of pyrimidine metabolism on immune checkpoints, tumor-infiltrating immune cells, and cytokine levels and determined the potential implications of pyrimidine metabolism in BC immunotherapy. In conclusion, our findings suggest that pyrimidine metabolism has underlying prognostic significance in BC and can facilitate a new management approach for patients with different prognoses and more precise immunotherapy.

Integrated bioinformatics analysis and experimental validation reveals fatty acid metabolism-related prognostic signature and immune responses for uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is the third most common gynecologic malignancy. Fatty acid metabolism (FAM) is an essential metabolic process in the immune microenvironment that occurs reprogramming in the presence of tumor signaling and nutrient competition. This study aimed to identify the fatty acid metabolism-related genes (FAMGs) to develop a risk signature for predicting UCEC.

Methods

The differentially expressed FAMGs between UCEC samples and controls from TCGA database were discovered. A prognostic signature was then constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Based on the median risk score, UCEC samples were categorized into high- and low-FAMGs groups. Kaplan-Meier (K-M) curve was applied to determine patients’ overall survival (OS). The independent prognostic value was assessed by uni- and multivariate analyses. The associations between the risk score and immune status, immune score, and drug resistance were evaluated. Quantitative Real-time PCR (qRT-PCR) was utilized to confirm FAMGs expression levels in UCEC cells.

Results

We built a 10-FAMGs prognostic signature and examined the gene mutation and copy number variations (CNV). Patients with a high-FAMGs had a worse prognosis compared to low-FAMGs patients in TCGA train and test sets. We demonstrated that FAMGs-based risk signature was a significant independent prognostic predictor of UCEC. A nomogram was also created incorporating this risk model and clinicopathological features, with high prognostic performance for UCEC. The immune status of each group was varied, and immune score was higher in a low-FAMGs group. HLA-related genes such as DRB1, DMA, DMB, and DQB2 had higher expression levels in the low-FAMGs group. Meanwhile, high-FAMGs patients were likely to response more strongly to the targeted drugs Bortezomib, Foretinib and Gefitinib. The qRT-PCR evidence further verified the significant expression of FAMGs in this signature.

Conclusions

A FAMGs-based risk signature might be considered as an independent prognostic indicator to predict UCEC prognosis, evaluate immune status and provide a new direction for therapeutic strategies.

A multifaceted and feasible prognostic model of amino acid metabolism-related genes in the immune response and tumor microenvironment of head and neck squamous cell carcinomas

We investigated the role of amino acid metabolism (AAM) in head and neck squamous cell carcinoma (HNSCC) tissues to explore its prognostic value and potential therapeutic strategies. A risk score based on four AAM-related genes (AMG) was constructed that could predict the prognosis of HNSCC. These four genes were up-regulated in HNSCC tissues and might act as oncogenes. Internal validation in The Cancer Genome Atlas (TCGA) by bootstrapping showed that patients with high-risk scores had a poorer prognosis than patients with low-risk scores, and this was confirmed in the Gene Expression Omnibus (GEO) cohort. There were also differences between the high-risk and low-risk groups in clinical information and different anatomical sites such as age, sex, TNM stage, grade stage, surgery or no surgery, chemotherapy, radiotherapy, no radiotherapy, neck lymph node dissection or not, and neck lymphovascular invasion, larynx, overlapping lesion of lip, and oral cavity and pharynx tonsil of overall survival (OS). Immune-related characteristics, tumor microenvironment (TME) characteristics, and immunotherapy response were significantly different between high- and low-risk groups. The four AMGs were also found to be associated with the expression of markers of various immune cell subpopulations. Therefore, our comprehensive approach revealed the characterization of AAM in HNSCC to predict prognosis and guide clinical therapy.

Single-nucleus RNA sequencing reveals the shared mechanisms inducing cognitive impairment between COVID-19 and Alzheimer’s disease

Alzheimer’s disease (AD)-like cognitive impairment, a kind of Neuro-COVID syndrome, is a reported complication of SARS-CoV-2 infection. However, the specific mechanisms remain largely unknown. Here, we integrated single-nucleus RNA-sequencing data to explore the potential shared genes and pathways that may lead to cognitive dysfunction in AD and COVID-19. We also constructed ingenuity AD-high-risk scores based on AD-high-risk genes from transcriptomic, proteomic, and Genome-Wide Association Studies (GWAS) data to identify disease-associated cell subtypes and potential targets in COVID-19 patients. We demonstrated that the primary disturbed cell populations were astrocytes and neurons between the above two dis-eases that exhibit cognitive impairment. We identified significant relationships between COVID-19 and AD involving synaptic dysfunction, neuronal damage, and neuroinflammation. Our findings may provide new insight for future studies to identify novel targets for preventive and therapeutic interventions in COVID-19 patients.

Aging-related features predict prognosis and immunotherapy efficacy in hepatocellular carcinoma

The aging microenvironment serves important roles in cancers. However, most studies focus on circumscribed hot spots such as immunity and metabolism. Thus, it is well ignored that the aging microenvironment contributes to the proliferation of tumor. Herein, we established three prognosis-distinctive aging microenvironment subtypes, including AME1, AME2, and AME3, based on aging-related genes and characterized them with “Immune Exclusion,” “Immune Infiltration,” and “Immune Intermediate” features separately. AME2-subtype tumors were characterized by specific activation of immune cells and were most likely to be sensitive to immunotherapy. AME1-subtype tumors were characterized by inhibition of immune cells with high proportion of Catenin Beta 1 (CTNNB1) mutation, which was more likely to be insensitive to immunotherapy. Furthermore, we found that CTNNB1 may inhibit the expression of C-C Motif Chemokine Ligand 19 (CCL19), thus restraining immune cells and attenuating the sensitivity to immunotherapy. Finally, we also established a robust aging prognostic model to predict the prognosis of patients with hepatocellular carcinoma. Overall, this research promotes a comprehensive understanding about the aging microenvironment and immunity in hepatocellular carcinoma and may provide potential therapeutic targets for immunotherapy.

Landscape of prognosis and immunotherapy responsiveness under tumor glycosylation-related lncRNA patterns in breast cancer

Aberrant glycosylation, a post-translational modification of proteins, is regarded to engage in tumorigenesis and malignant progression of breast cancer (BC). The altered expression of glycosyltransferases causes abnormal glycan biosynthesis changes, which can serve as diagnostic hallmarks in BC. This study attempts to establish a predictive signature based on glycosyltransferase-related lncRNAs (GT-lncRNAs) in BC prognosis and response to immune checkpoint inhibitors (ICIs) treatment. We firstly screened out characterized glycosyltransferase-related genes (GTGs) through NMF and WGCNA analysis and identified GT-lncRNAs through co-expression analysis. By using the coefficients of 8 GT-lncRNAs, a risk score was calculated and its median value divided BC patients into high- and low-risk groups. The analyses unraveled that patients in the high-risk group had shorter survival and the risk score was an independent predictor of BC prognosis. Besides, the predictive efficacy of our risk score was higher than other published models. Moreover, ESTIMATE analysis, immunophenoscore (IPS), and SubMAP analysis showed that the risk score could stratify patients with distinct immune infiltration, and patients in the high-risk group might benefit more from ICIs treatment. Finally, the vitro assay showed that MIR4435-2HG might promote the proliferation and migration of BC cells, facilitate the polarization of M1 into M2 macrophages, enhance the migration of macrophages and increase the PD-1/PD-L1/CTLA4 expression. Collectively, our well-constructed prognostic signature with GT-lncRNAs had the ability to identify two subtypes with different survival state and responses to immune therapy, which will provide reliable tools for predicting BC outcomes and making rational follow-up strategies.

Establishment of lactate-metabolism-related signature to predict prognosis and immunotherapy response in patients with colon adenocarcinoma

The outcome of colon adenocarcinoma (COAD) patients remains dismal, and lactate metabolism has been characterized to promote tumor development and immune evasion. Based on the above background, it is worthwhile to explore novel prognostic and therapeutic biomarkers for COAD patients from the aspect of lactate metabolism. Above all, 228 available lactate-metabolism-related genes (LMRGs) were acquired, and the landscape of copy number variation and the expression difference of mRNA levels between colon normal and tumor samples were investigated among these LMRGs. Importantly, eight overall survival (OS)-involved LMRGs were then distinguished by means of univariate Cox regression analysis in both GSE40967 and TCGA-COAD data sets. Subsequently, prognostic risk scores were established, integrating seven OS-related LMRGs by LASSO Cox regression analysis in the GSE40967 set, and then verified in the TCGA-COAD cohort. From the comprehensive analyses, COAD patients with high risk had comparatively more inferior survival probability in all populations of the study, and they tended to have more severe clinicopathological features with the risk score increasing. Moreover, by integrating age, AJCC T and pathological stage, and risk score, we constructed a prognostic nomogram that demonstrated great prediction effectiveness for OS of COAD patients. Furthermore, the potential effect of various risk score on tumor immune was assessed from enrichment of immune-related pathways, tumor-infiltrating immune cells, and expression levels of immune checkpoints separately. We could draw a conclusion that COAD patients with higher lactate-metabolism-related risk scores may acquire an immunosuppressive tumor microenvironment, which subsequently led to immune escapes and poor prognoses. Conclusively, all findings in the present study illustrate a great prognostic value of the lactate-metabolism-related risk signature, providing more in-depth insights into the indispensable function of lactate metabolism in prognosis and tumor immunity of COAD.

Pan-cancer analysis reveals interleukin-17 family members as biomarkers in the prediction for immune checkpoint inhibitor curative effect

Background

The interleukin-17 (IL-17) family contains six homologous genes, IL-17A to IL-17F. Growing evidence indicates that dysregulated IL-17 family members act as major pathogenic factors in the early and late stages of cancer development and progression. However, the prevalence and predictive value of IL-17 for immune checkpoint inhibitor (ICI) therapeutic effectiveness in multiple tumor types remain largely unknown, and the associations between its expression levels and immunotherapy-associated signatures also need to be explored.

Methods

The pan-cancer dataset in The Cancer Genome Atlas (TCGA) was downloaded from UCSC Xena (http://xena.ucsc.edu/). The immunotherapeutic cohorts included IMvigor210, which were obtained from the Gene Expression Omnibus database and included in a previously published study. Other datasets, namely, the GEO dataset and PRECOG, GEO, and METABRIC databases, were also included. In 33 TCGA tumor types, a pan-cancer analysis was carried out including their expression map, clinical risk assessment, and immune subtype analysis, along with their association with the stemness indices, tumor microenvironment (TME) in pan-cancer, immune infiltration analysis, ICI-related immune indicators, and drug sensitivity. RT-PCR was also carried out to verify the gene expression levels among MCF-10A and MCF-7 cell lines.

Results

The expression of the IL-17 family is different between tumor and normal tissue in most cancers, and consistency has been observed between gene activity and gene expression. RT-PCR results show that the expression differences in the IL-17 family of human cell (MCF-10A and MCF-7) are consistent with the bioinformatics differential expression analysis. Moreover, the expression of the IL-17 family can be a sign of patients’ survival prognosis in some tumors and varies in different immune subtypes. Moreover, the expression of the IL-17 family presents a robust correlation with immune cell infiltration, ICI-related immune indicators, and drug sensitivity. High expression of the IL-17 family is significantly related to immune-relevant pathways, and the low expression of IL-17B means a better immunotherapeutic response in BLCA.

Conclusion

Collectively, IL-17 family members may act as biomarkers in predicting the prognosis of the tumor and the therapeutic effects of ICIs, which provides new guidance for cancer treatment.

Circular RNA KIF4A Promotes Liver Metastasis of Breast Cancer by Reprogramming Glucose Metabolism

Background

Circular RNAs (circRNAs) regulate complex functional processes and play crucial roles in cancer development and progression. It was reported that circKIF4 regulates the progression of triple-negative breast cancer (TNBC). This study evaluates the role of circKIF4 in breast cancer distant metastasis and metabolic reprogramming.

Methods

RT-qPCR was performed to verify the expression of circKIF4A in breast cancer, liver metastatic tissues, and cell lines. The function of circKIF4A in metastasis was evaluated both in vitro and in vivo through a series of experiments, including cell migration and glucose intake experiments. Additionally, we conducted molecular experiments to clarify the regulatory role of circKIF4A. We then conducted a Luciferase reporter assay and an RNA immunoprecipitation assay to identify the molecular interactions between circKIF4A and miRNA.

Results

circKIF4A was overexpressed in breast cancer cell lines and tissues, inhibiting its expression and suppressing breast cancer growth and metastasis. Interestingly, we observed that circKIF4A reprogrammed the glucose metabolism of breast cancer, and silencing circKIF4A greatly affected glucose uptake and lactate production in breast cancer cells. miR-335 can be sponged by circKIF4A, which affected the expression of ALDOA/OCT4 protein and regulated HK2/PKM2 expression.

Conclusions

This study demonstrated that the circKIF4A-miR-335-OCT4/ALDOA-HK2/PKM2 axis is critical to breast cancer metabolic reprogramming, indicating that this axis could be a novel therapeutic target for the treatment of liver metastasis of breast cancer.

Identification of cuproptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer

Breast cancer (BC) is now the most frequent and lethal cancer among women. Cuproptosis is a newly identified programmed cell death process that has been connected to tumor therapeutic sensitivity, patient outcomes, and the genesis of cancer. Cuproptosis-related genes (CRGs) are involved in breast cancer, although their roles and potential mechanisms are still unclear. First, we examined the effect of gene mutations and copy number changes on overall survival in 1168 breast cancer samples. Breast cancer patients were split into two molecular categories as determined by the variation in CRG based on clinicopathological traits, overall survival, and cell-infiltrating traits in tumor microenvironments. In addition, we created and validated a CRG score to calculate breast cancer patients' OS. Finally, we created a comprehensive nomogram for the clinical use of the CRG score. Patients whose CRG scores were low showed increased odds of developing OS, a larger mutation load, and immunological activation than those with high CRG scores. The CRG score, the cancer stem cell index, and the responsiveness to chemotherapy or targeted therapies were also shown to be statistically significantly correlated. Our thorough examination of CRGs in breast cancer patients demonstrated that they may be useful predictors of prognosis, clinical characteristics, and tumor microenvironment. These findings provide fresh insight into CRGs in breast cancer and might inspire brand-new approaches to both diagnosing and treating patients there.

Comprehensive analysis of the endoplasmic reticulum stress-related long non-coding RNA in bladder cancer

Background

Bladder cancer is ranked the second most frequent tumor among urological malignancies. The research strived to establish a prognostic model based on endoplasmic reticulum stress (ERS)-related long non-coding RNA (lncRNA) in bladder cancer.

Methods

We extracted the ERS-related genes from the published research and bladder cancer data from the Cancer Genome Atlas database. ERS-related lncRNAs with prognostic significance were screened by univariate Cox regression, least absolute shrinkage and selection operator regression analysis and Kaplan-Meier method. Multivariate Cox analysis was leveraged to establish the risk score model. Moreover, an independent dataset, GSE31684, was used to validate the model’s efficacy. The nomogram was constructed based on the risk score and clinical variables. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the ERS-related signature. Finally, we employed external datasets (GSE55433 and GSE89006) and qRT-PCR to investigate the expression profile of these lncRNAs in bladder cancer tissues and cells.

Results

Six ERS-related lncRNAs were identified to be closely coupled with patients’ prognosis. On this foundation, a risk score model was created to generate the risk score for each patient. The ERS-related risk score was shown to be an independent prognostic factor. And the results of GSE31684 dataset also supported this conclusion. Then, a nomogram was constructed based on risk scores and clinical characteristics, and proven to have excellent predictive value. Moreover, the gene function analysis demonstrated that ERS-related lncRNAs were closely linked to fatty extracellular matrix, cytokines, cell adhesion, and tumor pathways. Further analysis revealed the association of the 6-lncRNAs signature with gene mutations and immunity in bladder cancer. Finally, the external datasets and qRT-PCR verified high expressions of the ERS-related lncRNAs in bladder cancer tissues and cells.

Conclusions

Overall, our findings indicated that ERS-related lncRNAs, which may affect tumor pathogenesis in a number of ways, might be exploited to assess the prognosis of bladder cancer patients.

Liquid Biopsy in Pre-Metastatic Niche: From Molecular Mechanism to Clinical Application

Metastatic dissemination represents a hallmark of cancer that is responsible for the high mortality rate. Recently, emerging evidence demonstrates a time-series event—pre-metastatic niche (PMN) has a profound impact on cancer metastasis. Exosomes, cell-free DNA (cfDNA), circulating tumor cells (CTC), and tumor microenvironment components, as critical components in PMN establishment, could be monitored by liquid biopsy. Intensive studies based on the molecular profile of liquid biopsy have made it a viable alternative to tissue biopsy. Meanwhile, the complex molecular mechanism and intercellular interaction are great challenges for applying liquid biopsy in clinical practice. This article reviews the cellular and molecular components involved in the establishment of the PMN and the promotion of metastasis, as well as the mechanisms of their interactions. Better knowledge of the characteristics of the PMN may facilitate the application of liquid biopsy for clinical diagnosis, prognosis, and treatment.